--- layout: post title: Animating plots in R subtitle: Enchancing ggplot2 capabilities! tags: [R, tidyverse] comments: true --- 2023-12-12-r-animation.knit

Uses of Animation in R

Many R users are already familiar with ggplot2, one of the packages in the amazing tidyverse. ggplot2 elevates plots with capabilities well beyond base R plotting. I have always wanted to play around with plot animation, but it has never been compatible with the medium I use most often to present plots: reports or journal articles. Though not appropriate to every medium, animated plots can breathe life into a website or powerpoint.

As is the beauty of R, there are many paths to achieve the same goal. In this post, I will explore two packages (gganimate and plotly), which can be used to animate plots created with ggplot2. If you are unfamiliar with ggplot2, I recommend you begin by checking out some of the resources linked at the bottom of the page!


Data Selection

With this exercise, I wanted to emphasize the ability of animation to highlight one subset of data at a time. The first use that jumps to mind is annual data, which is appropriate because it is ordinal—meaning it has an order that matters (e.g., 2001 is preceded by 2000 and succeeded by 2002)—and also discrete—meaning there is a logical way to subset the data.

Data: World Population and Life Expectancy by Country, 1952–2007.

Source: Free data from UN POP via GAPMINDER.ORG (License: CC-BY 4.0). Retrieved through the gapminder package in R.

Dataset description: gapminder is a helpful tool for teaching data wrangling and visualization in R. It includes an excerpt of the data available at Gapminder.org, specifically: life expectancy, GDP per capita, and population for 142 countries every five years (1952 to 2007).

{: .warning-note} Warning from the package author: “this package exists for the purpose of teaching and making code examples. It is an excerpt of data found in specific spreadsheets on Gapminder.org circa 2010. It is not a definitive source of socioeconomic data and I don’t update it. Use other data sources if it’s important to have the current best estimate of these statistics.”


Animation using gganimate

gganimate (developed by Thomas Lin Pedersen and David Robinson) is an extension to ggplot2 which adds new “grammar” to define animation in the final plot.

{: .box-note} Note: The original iteration of gganimate (versions up to and including v0.1.1) is now deprecated and code written for the old API will not work with the new iteration of gganimate (v1.0.0 and beyond). This post will use grammar intended to work with the newer iteration of gganimate.

Let’s create a static plot showing the life expectancy of each country as a function of the population, with the countries grouped by continent:

library(ggplot2)
library(gapminder)

ggplot(gapminder, aes(pop, lifeExp, colour = country)) +
  geom_point(show.legend = FALSE, size = 1.5) +
  scale_colour_manual(values = country_colors) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw()


Now add an animation with gganimate. Here, I define that the frames I want to cycle through are a time series. This creates a new variable called “frame_time” which can be used to create a label for the plot.

library(gganimate)

ggplot(gapminder, aes(pop, lifeExp, colour = country)) +
  geom_point(show.legend = FALSE, size = 1.5) +
  scale_colour_manual(values = country_colors) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw() +
  labs(title = "Year: {closest_state}", x = "Population", y = "Life Expectancy") +
  transition_time(year) +
  ease_aes("cubic-in-out")


Alternatively, becuase there is only data avilable for every 5 years, I can define the frames as sepearate states, which makes the animation less focused on a smooth transition which “fills in” the gaps in the 5-year periods, and places more emphasis on each 5-year datapoint. Now, we have three options for the label: “previous_state,” “next_state,” or “closest_state.”

ggplot(gapminder, aes(pop, lifeExp, colour = country)) +
  geom_point(show.legend = FALSE, size = 1.5) +
  scale_colour_manual(values = country_colors) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw() +
  labs(title = "Year: {closest_state}", x = "Population", y = "Life Expectancy") +
  transition_states(year, transition_length = 2, state_length = 4) +
  ease_aes("cubic-in-out") +
  exit_fade(alpha = 0)

A full list of gganimate functions to customize aesthetics can be found here.


Animation using plotly: “ggplotly” function

plotly is an R package for “creating interactive web-based graphs via the open source JavaScript graphing library plotly.js.” plotly makes ggplots interactive, which allows for some exciting functionality if you are creating visualizations for a website. Compared to gganimate for animation purposes, plotly really shines by addition of an interactive slider alongside your plot, which allows the viewer to select and focus on a specific frame in the animation. It also includes a suite of interactive features that can be used for static or animated plots.

Unlike gganimate, which adds new grammar to ggplot, plotly is designed to work off of a ggplot which has already been created. We will use the same base ggplot code to create a static graph, with one addition: a key called “frame”, which defines the list of frames that are cycled through in the animation. We can add this variable when creating the plot. “frame” will not be recognized by ggplot2, but it will be recognized by the plotly function “ggplotly”.


library(ggplot2)
library(gapminder)
library(plotly)

p <- ggplot(gapminder, aes(pop, lifeExp, colour = country)) +
  geom_point(aes(frame = year), size = 1.5) +
  scale_colour_manual(values = country_colors) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw()

ggplotly(p) %>%
  layout(showlegend = FALSE)
# We no longer need the hide legend syntax in ggplot, because it is overriden by plotly.
# Instead, we can opt to hide the legend in the ggplotly syntax.


Let’s fine-tune the visualization by adjusting the button and slider options.

p <- ggplot(gapminder, aes(pop, lifeExp, colour = country)) +
  geom_point(aes(frame = year), size = 1.5) +
  scale_colour_manual(values = country_colors) +
  scale_x_log10() +
  facet_wrap(~continent) +
  theme_bw() +
  labs(x = "Population", y = "Life Expectancy")

# Using pipes (%>%) here, which is a dplyr function
ggplotly(p) %>%
  layout(showlegend = FALSE) %>%
  animation_slider(currentvalue = list(prefix = "Source: UN POP, Year: ")) %>%
  animation_button(x = 0.9, y = 0.4)

In-depth guides to customize visualizations made with plotly can be found here. Some useful starting guides include modifying legends and interaction configuration options.


Summary

Animation is a simple yet effective way to improve viewer engagement and interpretation of plots presented in a digital medium. For animations made in R, there are two important packages. The gganimate package expands on ggplot2 grammar to introduce animation, which can be rendered as a gif or video. The plotly package can also be used to animate plots created with ggplot2, but it can also add interactive elements to web-based (HTML) visualizations.

Learning Roundup

New Skills Refresher Skills
Animation in R Plotting in R
GitHub Markdown
Visual Studio Code Creative writing

Software Used

R version: 4.3.2

RStudio version: 2023.09.1+494

R Packages: gapminder (v1.0.0); ggplot2 (v3.4.4); gganimate (v1.0.8); gifski (v1.12.0-2); plotly (v4.10.3)

{: .box-note} Note: gifski is a renderer, which is required if you want the output of gganimate to be a gif rather each frame as a seperate image. If you prefer a video output over a gif, you can use an alternate renderer (such as the av package) and specify it in your code to override the deafult use of gifski. However, a renderer is required to combine the frames into an animation.


Resources